vcov.fixest {fixest} | R Documentation

## Extracts the variance/covariance of a `femlm` fit

### Description

This function extracts the variance-covariance of estimated parameters from a model estimated with `femlm`, `feols` or `feglm`.

### Usage

    ## S3 method for class 'fixest'
    vcov(object, se, cluster, dof = TRUE,
      exact_dof = FALSE, forceCovariance = FALSE, keepBounded = FALSE,
      ...)

### Arguments

`object` | A `fixest` object. Obtained using the functions `femlm`, `feols` or `feglm`.
`se` | Character scalar. Which kind of standard error should be computed: “standard”, “White”, “cluster”, “twoway”, “threeway” or “fourway”? By default if there are clusters in the estimation: `se = "cluster"`, otherwise `se = "standard"`. Note that this argument can be implicitly deduced from the argument `cluster`.

`cluster`

Tells how to cluster the standard-errors (if clustering is requested). Can be either a list of vectors, a character vector of variable names, a formula or an integer vector. Assume we want to perform 2-way clustering over `var1` and `var2` contained in the data.frame `base` used for the estimation. All the following `cluster` arguments are valid and do the same thing: `cluster = base[, c("var1, "var2")]}, \code{cluster = c("var1, "var2")`, `cluster = ~var1+var2`. If the two variables were used as clusters in the estimation, you could further use `cluster = 1:2` or leave it blank with `se = "twoway"` (assuming `var1` [resp. `var2`] was the 1st [res. 2nd] cluster).

`dof` | Logical, default is `TRUE`. Should there be a degree of freedom correction to the standard errors of the coefficients?
`exact_dof` | Logical, default is `FALSE`. In case there were 2+ clusters in the estimation, it computes the exact number of degrees of freedom (this is not needed in case of balanced panels).
`forceCovariance` | (Advanced users.) Logical, default is `FALSE`. In the peculiar case where the obtained Hessian is not invertible (usually because of collinearity of some variables), use this option to force the covariance matrix, by using a generalized inverse of the Hessian. This can be useful to spot where possible problems come from.
`keepBounded` | (Advanced users – feNmlm with non-linear part and bounded coefficients only.) Logical, default is `FALSE`. If `TRUE`, then the bounded coefficients (if any) are treated as unrestricted coefficients and their S.E. is computed (otherwise it is not).
`...` | Other arguments to be passed to `summary.fixest`. The computation of the VCOV matrix is first done in `summary.fixest`.

### Value

It returns a N\times N square matrix where N is the number of variables of the fitted model. This matrix has an attribute “type” specifying how this variance/covariance matrix has been computed (i.e. was it created using White correction, or was it clustered along a specific factor, etc).

### Author(s)

Laurent Berge

### See Also

See also the main estimation functions `femlm`, `feols` or `feglm`. `summary.fixest`, `confint.fixest`, `resid.fixest`, `predict.fixest`, `fixef.fixest`.

### Examples

    # Load trade data
    data(trade)

    # We estimate the effect of distance on trade (with 3 fixed-effects)
    est_pois = femlm(Euros ~ log(dist_km) + log(Year) | Origin + Destination +
                     Product, trade)

    # By default, in the presence of FEs
    # the VCOV is clustered along the first FE
    vcov(est_pois)

    # "white" VCOV
    vcov(est_pois, se = "white")

    # "clustered" VCOV (with respect to the Product factor)
    vcov(est_pois, se = "cluster", cluster = trade$Product)
    # another way to make the same request:
    # note that previously arg. se was optional since deduced from arg. cluster
    vcov(est_pois, cluster = "Product")
    # yet another way:
    vcov(est_pois, cluster = ~Product)

    # Another estimation without cluster:
    est_pois_simple = femlm(Euros ~ log(dist_km) + log(Year), trade)

    # We can still get the clustered VCOV,
    # but we need to give the argument cluster:
    vcov(est_pois_simple, cluster = ~Product)

[Package fixest version 0.1.0 Index]